Testing axial symmetry and separability of lattice processes
By Luisa Scaccia, R. J. Martin. 2005
62 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Food Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2635729a2630191e41d0e57a5aaf10d1261e8e7f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marc G. Genton
- Tilmann Gneiting
- Peter Guttorp
- Montserrat Fuentes
- Brian J. Reich
- Martin Schlather
- Mikyoung Jun
- Tatiyana V. Apanasovich
In Food Science
In Statistics and Probability
- Marc G. Genton
- Tilmann Gneiting
- Montserrat Fuentes
- Peter Guttorp
- Brian J. Reich
- James W. Taylor
- Reinaldo Boris Arellano-Valle
- Adelchi Azzalini
Citations in the corpus
- Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry (2007, methodology): “…have been proposed recently and are based on parametric models (Shitan and Brockwell 1995; Guo and Billard 1998; Brown, K̊aresen, Roberts and Tonellato 2000), likelihood ratio tests and subsampling (Mitchell, Genton and Gumpertz 2004) or spectral methods (Scaccia and Martin 2005; Fuentes 2005a).”
- Spatial–temporal mesoscale modeling of rainfall intensity using gage and radar data (2008, background): “In the purely spatial context, this property is also known as axial symmetry [Scaccia and Martin (2005)] or reflection symmetry [Lu and Zimmerman (2005)].”
- Separable approximations of space‐time covariance matrices (2007, background): “Matsuda and Yajima (2004), Scaccia and Martin (2005), and Fuentes (2006) suggested a test for separability of covariances based on spectral methods.”